Systems and methods for monitoring impact on electric aircraft

ABSTRACT

A system for monitoring impact on an electric aircraft is presented. The system includes a sensor communicatively connected to a flight component, wherein the sensor is configured to detect a measured force datum and generate an impact datum as a function of the measured force datum. The system further includes a computing device configured to simulate a landing performance model output as a function of the impact datum, generate a landing performance datum as a function of a comparison between the landing performance model output, determine an alert datum as a function of the landing performance datum, and display the landing performance datum on a user device.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to systemsand methods for monitoring impact on electric aircraft.

BACKGROUND

Although landing of a flying vehicle is usually the final stage of aflight, it is arguable the most crucial stage in ensuring the completionof a flight. The mechanical components involved in ensuring an electricaircraft lands successfully are crucial. Throughout an electricaircraft's lifecycle, those mechanical parts may experience wear andtear, in which poorly maintained and monitored performances of thosemechanical parts may lead to potentially hazardous incidents involvingthe electric aircraft.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for monitoring impact on an electric aircraft ispresented. The system includes a sensor communicatively connected to aflight component, wherein the sensor is configured to detect a measuredforce datum and generate an impact datum as a function of the measuredforce datum. The system further includes a computing device configuredto simulate a landing performance model output as a function of theimpact datum, generate a landing performance datum as a function of acomparison between the landing performance model output, determine analert datum as a function of the landing performance datum, and displaythe landing performance datum on a user device.

In another aspect, a method for monitoring impact on an electricaircraft is presented. The method includes detecting, by a sensorcommunicatively connected to a flight component, a measured force datum,generating an impact datum as a function of the measured force datum,simulating, by a computing device, a landing performance model output asa function of the impact datum, generating a landing performance datumas a function of a comparison between the landing performance modeloutput, determining an alert datum as a function of the landingperformance datum, and displaying the landing performance datum on auser device.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system formonitoring impact on an electric aircraft;

FIG. 2 is a diagrammatic representation of an exemplary embodiments offuzzy sets for an impact threshold;

FIG. 3 is a diagrammatic representation of an exemplary embodiments ofbivalent sets for an impact threshold;

FIG. 4 is a schematic representation of an exemplary electric aircraft;

FIG. 5 is flow diagram of an exemplary embodiment of a method formonitoring impact on an electric aircraft;

FIG. 6 is a block diagram illustrating an exemplary flight controller;

FIG. 7 illustrates a block diagram of an exemplary machine-learningprocess; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for monitoring impact on an electric aircraft. In anembodiment, the electric aircraft may include an electric verticaltake-off and landing (eVTOL) aircraft. In an embodiment, sensors may beconnected to the actuators of the electric aircraft such as the landinggears. The landing gears may include wheels, skids, or any mechanismthat absorbs the force of the electric aircraft when landing on asurface. Aspects of the present disclosure can be used to measure forceor strain of the landing gears of the electric aircraft. This is so, atleast in part, to identify the health statues of the landing gears ofthe electric aircraft and determine the flight safety of the electricaircraft. In another embodiment, aspects of the present disclosure caninclude using sensors to detect the landing zone of the electricaircraft to determine or predict the level of force and/or damage thelanding gears of the electric aircraft may endure. This is so, at leastin part, wherein the electric aircraft may land on unregulated landingdestinations without landing pads for the electric aircraft to land on.The electric aircraft may be required to make emergency or impromptulandings on uneven or dangerous terrain. In another embodiment, aspectsof the present disclosure may alert or notify a user or pilot of damagedor degraded landing gears requiring imminent attention. This is so, atleast in part, for the pilot for the pilot to be alerted of thepotential dangers that may be resulted from the degraded landing gearsand act appropriately. In some embodiments, the computing device mayautomatically program the electric aircraft to land at a landing padclosest to its location for immediate maintenance of the landing gears.In another embodiment, the computing device may generate a trigger forsome emergency response that the pilot may initiate to safely land theelectric aircraft. In another embodiment, a remotely located userutilizing a remotely located user device may do the same things asdescribed above.

Aspects of the present disclosure can also be used to determine whetherthe landing gears of the present disclosure are safe enough to eithercontinue operations or require replacement/repairs. This is so, at leastin part, to ensure the following flights of the electric aircraft withdamaged or degraded landing gears can be resolved prior to the electricaircraft taking flight. Aspects of the present disclosure can also beused to generate predictive models to determine the level of danger thelanding gears of an electric aircraft poses based on the performance andstatus of the landing gears from previous flights and landings. In anembodiment, a computing device may compare the landing data to previouslanding data of the electric aircraft. The computing device may detectsome discrepancies or level of change in performance qualities of thelanding gears from previously collected data to accurately predict thefuture performance quality of the landing gears. This is so, at least inpart, for the computing device to discern whether or not the landinggears are more prone to serious damage or cause serious damage. Thepreviously collected data may be retrieved from a local/cloud databasewherein the computing device records every landing data from everyflight/landing of the electric aircraft in order to create a robustdatabase for further data analysis and manipulation. The computingdevice may incorporate any machine-learning models. In anotherembodiment, the sensors can detect the force exerted or absorbed by thelanding gears during take-off to further determine the landing safety ofthe electric aircraft, even during mid-flight. Aspects of the presentdisclosure can be used to analyze sensor data to determine landingsafety of the electric aircraft and its landing gears during any stageor flight phase of the electric aircraft.

Aspects of the present disclosure can be used to monitor the landing ofthe electric aircraft as well as the data collected by its sensors via auser device. In an embodiment, the user device may be remotely locatedfrom the electric aircraft and used to analyze data collected by thesensors of the electric aircraft. This is so, at least in part, for auser such as a fleet manager to view the performances and health statusof the electric aircraft and its flight components in a safe location.Aspects of the present disclosure can also be used to monitor thelanding of the aircraft as well as its data via a user device locatedinside the electric aircraft. The user device may include a pilot devicesuch as a multi-function display integrated into the cockpit of theelectric aircraft and its flight instruments. This is so, at least inpart, for the pilot to also monitor and analyze the data collected byits sensors and pilot the electric aircraft accordingly. In someembodiments, the pilot may discover some potential danger as a result ofthe degradation of the landing gears and maneuver the electric aircraftadaptively based on the data. Exemplary embodiments illustrating aspectsof the present disclosure are described below in the context of severalspecific examples.

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper,” “lower,” “left,” “rear,” “right,”“front,” “vertical,” “horizontal,” “inner,” “outer,” and derivativesthereof shall relate to the invention as oriented in FIG. 1 .Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description. It is also to beunderstood that the specific devices and processes illustrated in theattached drawings, and described in the following specification, aresimply embodiments of the inventive concepts defined in the appendedclaims. Hence, specific dimensions and other physical characteristicsrelating to the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 formonitoring impact on an electric aircraft is illustrated. In anon-limiting embodiment, system 100 may include any electric aircraftsuch as an eVTOL, an unmanned aerial vehicle (UAV), a drone, an electrichelicopter, and the like thereof. The electric aircraft includes aflight component 108. A “flight component,” as used in this disclosure“flight component” is a portion of an aircraft that can be moved oradjusted to affect one or more flight elements. In some embodiments,flight component 108 may include electric motors, electric propulsors,electric propulsion systems, wings, alerions, rudders, forward pushers,propellers, etc. In a non-limiting embodiment, flight component 108 mayinclude a landing gear. A “landing gear,” as used in this disclosure, isan undercarriage of an electric aircraft used to support take-off and/orlanding of the electric aircraft. The landing gear may include skis,floats, wheels, skids, and the like thereof. The landing gear mayinclude retractable gears and shock absorbers.

With continued reference to FIG. 1 , sensor 104 may be communicativelyconnected to flight component 108. A “sensor,” for the purposes of thisdisclosure, is an electronic device configured to detect, capture,measure, or combination thereof, a plurality of external and electricaircraft component quantities. Sensor 104 may include any computingdevice as described in the entirety of this disclosure and configured toconvert and/or translate a plurality of signals detected into electricalsignals for further analysis and/or manipulation. In a non-limitingembodiment, sensor 104 may include a plurality of sensors comprised in asensor suite. For example and without limitation, sensor 104 may includeflow sensors, temperature sensors, altimeters, pressure sensors,proximity sensors, airspeed indicators, position sensors, globalpositioning system (GPS), humidity sensors, level sensors, gas sensors,wireless sensor networks (WSN), compasses, magnetometers, altitudeheading and reference systems (AHRSes), tachometers, etc. In anon-limiting embodiment, sensor 104 may be communicatively connected tothe electric aircraft of system 100. As used in this disclosure,“communicatively connected” is defined as a process whereby one device,component, or circuit is able to receive data from and/or transmit datato another device, component, or circuit; communicative connecting maybe performed by wired or wireless electronic communication, eitherdirectly or by way of one or more intervening devices or components. Inan embodiment, communicative connecting includes electrically couplingan output of one device, component, or circuit to an input of anotherdevice, component, or circuit. Communicative connecting may includeindirect connections via “wireless” connection, low power wide areanetwork, radio communication, optical communication, magnetic,capacitive, or optical coupling, or the like. At least pilot control mayinclude buttons, switches, or other binary inputs in addition to, oralternatively than digital controls about which a plurality of inputsmay be received. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of the various embodiments ofcontrolling a cursor for visual data manipulation for purposes asdescribed herein. Persons skilled in the art, upon reviewing theentirety of this disclosure, will also be aware of the various warningsymbols that may be employed to grab the attention of a pilot in thecontext of simulation consistently described in the entirety of thisdisclosure.

With continued reference to FIG. 1 , sensor 104 may include a motionsensor. A “motion sensor,” for the purposes of this disclosure is adevice or component configured to detect physical movement of an objector grouping of objects. One of ordinary skill in the art wouldappreciate, after reviewing the entirety of this disclosure, that motionmay include a plurality of types including but not limited to: spinning,rotating, oscillating, gyrating, jumping, sliding, reciprocating, or thelike. Sensor 104 may include, but not limited to, torque sensor,gyroscope, accelerometer, magnetometer, inertial measurement unit (IMU),pressure sensor, force sensor, proximity sensor, displacement sensor,vibration sensor, and the like. In a non-limiting embodiment, sensor 104may use of active range finding methods which may include, but notlimited to, light detection and ranging (LIDAR), radar, sonar,ultrasonic range finding, forward-looking infrared (FLIR) cameras,enhanced vision systems (EVS), short wave infrared (SWIR) imagers, orthe like thereof.

With continued reference to FIG. 1 , sensor 104 may include athree-dimensional (3D) scanner. 4D scanner may include the use of 4Dlaser scanning. 4D Laser Scanning is a non-contact, non-destructivetechnology that digitally captures the shape of physical objects using aline of laser light. 4D laser scanners create “point clouds” of datafrom the surface of an object. In other words, 4D laser scanning is away to capture a physical object's exact size and shape into thecomputer world as a digital 4-dimensional representation. 4D laserscanners measure fine details and capture free-form shapes to quicklygenerate highly accurate point clouds. 4D laser scanning is ideallysuited to the measurement and inspection of contoured surfaces andcomplex geometries which require massive amounts of data for theiraccurate description and where doing this is impractical with the use oftraditional measurement methods or a touch probe. In a non-limitingembodiment, a 4D scanner may capture a potential landing zone andgenerate a 4D model of a plot representing the landing zone for analysisdescribed later in the disclosure.

With continued reference to FIG. 1 , sensor 104 may be configured todetect and/or determine a plurality of ranges of an object with a laser.Determining ranges may include a technique for the measuring ofdistances or slant range from an observer including at least a sensor104 to a target which may include a potential landing zone. A “potentiallanding zone,” as used in this disclosure, is one or more optionsdenoting a landing destination for the electric aircraft. The potentiallanding zone may include any area encompassing a surface for theelectric aircraft to land on. The potential landing zone may include anyinfrastructure to support the electric aircraft such as landing pad,launch pad, charging station, and the like thereof. Technique mayinclude the use of active range finding methods which may include, butnot limited to, light detection and ranging (LIDAR), radar, sonar,ultrasonic range finding, and the like. In a non-limiting embodiment,sensor 104 may include at least a LIDAR system to measure rangesincluding variable distances from sensor 104 to a potential landingzone. LIDAR systems may include, but not limited to, a laser, at least aphased array, at least a microelectromechanical machine, at least ascanner and/or optic, a photodetector, a specialized GPS receiver, andthe like. In a non-limiting embodiment, sensor 104 including a LIDARsystem may targe an object including a potential landing zone 112 with alaser and measure the time for at least a reflected light to return tothe LIDAR system. LIDAR may also be used to make digital 4-Drepresentations of areas on the earth's surface and ocean bottom, due todifferences in laser return times, and by varying laser wavelengths. Ina non-limiting embodiment the LIDAR system may include a topographicLIDAR and a bathymetric LIDAR, wherein the topographic LIDAR that mayuse near-infrared laser to map a plot of a land or surface representinga potential landing zone while the bathymetric LIDAR may usewater-penetrating green light to measure seafloor and various waterlevel elevations within and/or surrounding the potential landing zone.In a non-limiting embodiment, electric aircraft may use at least a LIDARsystem as a means of obstacle detection and avoidance to navigate safelythrough environments to reach a potential landing zone. In anon-limiting embodiment, the LIDAR may be used to measure the surface ofthe landing location of the electric aircraft wherein the data collectedmay be used to determine the potential damages, degradation, physicalwear and tear, or the combination thereof, of flight component 108and/or the landing gear of the electric aircraft. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofthe using sensors to measure the qualities of the surface andenvironment for determining potential physical effects on the electricaircraft in the context of monitoring landing.

Still referring to FIG. 1 , sensor 104 may be installed onto a pluralityof flight instruments of the electric aircraft. As used in thisdisclosure, a “flight instrument,” is defined as an instrument used toprovide information involving the flight situation of an electricaircraft it is installed on. In some embodiments, the information of theflight situation may include, but not limited to, altitude, attitude,airspeed, vertical speed, heading, and the like thereof. Sensor 104installed onto the flight instruments may include a gyroscope, a torquemonitor, tachometers, engine temperature gauges, fuel- and oil-quantitygauges, pressure gauges, altimeters, airspeed-measurement meters,vertical speed indicators and/or combination thereof. In anotherembodiment, sensor 104 may include radars such as, doppler radars,lightning-detection radars, terrain radars, anti-collision warningsystems, stall warning systems, etc. In a non-limiting embodiment,various types of sensor 104 may be used to inform the pilot of theelectric aircraft or a user interacting with a remote device incommunication with the electric aircraft to take proper action andprecaution, and prevent any kind of disaster or accident. Anyinformation captured by sensor 104 may be used for ground testing,flight testing, vibration, environment, angle of attack, static, and thelike thereof Sensor 104 may include a sensor suite which may include aplurality of sensors, wherein the sensors may include any sensordescribed herein.

With continued reference to FIG. 1 , sensor 104 may be configured todetect measured aircraft data. A “measured aircraft data,” for thepurpose of this disclosure, are signals representing informationcaptured by sensor 104 or any sensor as described in the entirety ofthis disclosure. In a non-limiting embodiment, the measured aircraftdata may include temperature, voltage, wind resistance, pressure, speed,angles, acceleration, flight speed, flight angle, lag, thrust, lift, andthe like thereof. Sensor 104 may also detect a plurality of maneuverdata. A “plurality of maneuver data,” for the purpose of thisdisclosure, is any collection of information describing completion bythe pilot of procedures and concepts that control the electric aircraft,a simulated electric aircraft, and/or the simulator module. For exampleand without limitation, the plurality of maneuver data may includefoundational flight maneuvers, such as straight-and-level turns, climbsand descents, and/or performance maneuvers, such that the application offlight control pressures, attitudes, airspeeds, and orientations areconstantly changing throughout the maneuver. For example and withoutlimitation, the plurality of maneuver data may include, ground referencemaneuvers, such as turns around a point, s-turns, rectangular groundmaneuvering course, eights along A road, eights around pylons, hovertaxi, air taxi, surface taxi, and the like. As a further example andwithout limitation, the plurality of maneuver data may include takeoffsand landings, such as normal takeoff and climb, crosswind takeoff andclimb, short field takeoff and climb, normal takeoff from a hover,vertical takeoff to a hover, short field approach and landing, softfield approach and landing, touch and go, power-off 180 approach andlanding, normal approach to a hover, crosswind approach to the surface,and the like. The plurality of maneuver data may further include, forexample and without limitation, airborne maneuvers, such as trimming theaircraft, slow flight, lazy eights, chandelle, straight and levelflight, turns, steep turns, unusual attitudes, spatial disorientationdemonstration, hovering, hovering turn, rapid deceleration,reconnaissance procedures, and the like. The plurality of maneuver data,as a further non-limiting example, may include emergency preparedness,such as steep spirals, emergency approach and landing, spins, ditching,autorotation, vortex ring state, retreating blade stall, groundresonance, dynamic rollover, low rotor RPM, systems malfunction, flightdiversions, and the like. Further, the plurality of maneuver data mayinclude, as a non-limiting example, instrument procedures, such asaircraft holding procedures, arcing approach, instrument landing systemapproach, instrument reference climbs and descents, basic attitudeinstrument flight, and the like. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousprocedures and concepts that may represent the plurality of maneuverdata consistently with this disclosure.

Still referring to FIG. 1 , sensor 104 may be configured to identify aflight phase of the electric aircraft. As described in this disclosure,a “flight phase” is defined as a stage or period within a flight. In anon-limiting embodiment, the electric aircraft may undergo a pluralityof different flight phases in the course of a completion of a flight.For example and without limitation, the flight phases may include aplanning phase, lift-off/take-off phase, climb phase, cruise phase,descent phase, approach phase, taxi phase, hover phase, landing phase,and the like thereof. In an embodiment, sensor 104 may identify theflight phase of the electric aircraft as a function of the measuredaircraft data, such as the plurality of maneuver data. In a non-limitingembodiment, the pilot of the electric aircraft may perform variousflight maneuvers that result in the electric aircraft exerting power onvarious systems and flight components which is detected by sensor 104and identify the flight phase the electric aircraft is currently in orperforming. The pilot may perform the flight maneuvers using one or morepilot controls of the electric aircraft. Aa “pilot control,” for thepurpose of this disclosure, is an interactive mechanism or means whichallows a pilot to control operation of flight components of an electricaircraft. In a non-limiting embodiment, the pilot control may be used bya pilot to manipulate and/or command the components of an electricaircraft. In a non-limiting embodiment, the pilot control may becommunicatively connected to sensor 104 and receive a pilot input. A“pilot input” for the purpose of this disclosure, is as any gauge,throttle lever, clutch, dial, control, or any other mechanical orelectrical device that is configured to be manipulated by a pilot toreceive information. In a non-limiting embodiment, the pilot control maybe physically located in the cockpit of the aircraft or remotely locatedoutside of the aircraft in another location communicatively connected toat least a portion of the aircraft. The pilot control may includebuttons, switches, or other binary inputs in addition to, oralternatively than digital controls about which a plurality of inputsmay be received. In a non-limiting embodiment, the pilot input mayinclude a physical manipulation of a control like a pilot using a handand arm to push or pull a lever, or a pilot using a finger to manipulatea switch. In another non-limiting embodiment, the pilot input mayinclude a voice command by a pilot to a microphone and computing systemconsistent with the entirety of this disclosure. One of ordinary skillin the art, after reviewing the entirety of this disclosure, wouldappreciate that this is a non-exhaustive list of components andinteractions thereof that may include, represent, or constitute, or beconnected to sensor 104. In some cases, simulator module 120, thephysical cockpit, and the pilot control may include sensor 104 and/or becommunicatively connected to sensor 104. In a non-limiting embodiment,sensor 104 may be communicatively connected to computing device 112. Insome cases, sensor 104 may be configured to detect a user interactionwith pilot control. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of the various embodimentsand functions of the at least a pilot control for purposes as describedherein.

Still referring to FIG. 1 , sensor 104 may include a strain gauge. A“strain gauge,” as used in this disclosure, is a sensing device and/orsensor used to measure strain on an object such as flight component 108and/or the landing gear. for example and without limitation, the straingauge may include any strain gauge such as, but not limited to, linearstrain gauge, membrane rosette strain gauge, double linear strain gauge,shear strain gauge, column strain gauge, 45°-Rosette, 90°-Rosette, orcombination thereof. In another non-limiting embodiment, the straingauge may include piezoresistors, foil gauges, semiconductor straingauge, fiber optic sensor vibrating wire strain gauges, and the likethereof. The stain gauge may include any impact sensor as describedherein. In a non-limiting embodiment, the strain gauge may include anyconfiguration such as quarter, half, full bridge, or combinationthereof. The strain gauge may sense changes in strain on flightcomponent 108 as it absorbs the impact during landing and/or take-offfor an electric aircraft. The strain gauge may also measure, but notlimited to, variation in pressure, strain, stress, temperature, or thecombination thereof. In a non-limiting embodiment, a portion of flightcomponent 108 may change in size as a function of thermal expansionwhich will be detected as a strain by the strain gauge. Resistance ofthe strain gauge will change due to the thermal expansion. In anon-limiting embodiment, the strain gauge may be self-temperaturecompensated. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of the various embodiments of a straingauge in the context of monitoring impact of the electric aircraft whenlanding.

Still referring to FIG. 1 , sensor 104 and/or the strain gauge mayinclude at least a load cell. A “load cell,” as used in this disclosure,is a force transducer configured to convert force such as tension,compression, pressure, and/or torque, into electrical signals for anycomputing device to translate and/or standardize the electrical signalsfor analysis. The at least a load cell may include a pneumatic loadcell, hydraulic load cell, and the like thereof. In a non-limitingembodiment, the at least a load cell may include a plurality of typessuch as single point, planar beam, bending beam, sharing beam, dualshear beam, S-type, compression, ring torsion, spoke type, onboard, loadpins, weigh pads, or combination thereof. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of thevarious configurations for a force measuring and converting sensor forpurposes as described herein.

With continued reference to FIG. 1 , sensor 104 may be configured detectmeasured state data. A “measured state data,” as used in thisdisclosure, is a datum that is collected via a physical controller areanetwork (CAN) bus unit describing a plurality of functionalities of theelectric aircraft. A “physical controller area network bus,” as used inthis disclosure, is vehicle bus unit including a central processing unit(CPU), a CAN controller, and a transceiver designed to allow devices tocommunicate with each other's applications without the need of a hostcomputer which is located physically at the electric aircraft. In anon-limiting embodiment, the electric aircraft may include a pluralityof physical CAN bus units communicatively connected to the electricaircraft. For instance and without limitation, the physical CAN bus unitmay be consistent with the physical CAN bus unit in U.S. patentapplication Ser. No. 17/218,312 and entitled, “METHOD AND SYSTEM FORVIRTUALIZING A PLURALITY OF CONTROLLER AREA NETWORK BUS UNITSCOMMUNICATIVELY CONNECTED TO AN AIRCRAFT,” which is incorporated byreference herein in its entirety. In a non-limiting embodiment, themeasured state data may include a plurality of data signals detailing acontrol to one or more actuators communicatively connected to theaircraft. Measured state data may include a plurality of data entriesrelating aircraft pitch, roll, yaw, torque, angular velocity, climb,speed, performance, lift, thrust, drag, battery charge, fuel level,location, and the like. Measured state data may include a plurality ofdata communicating the status of flight control devices such asproportional-integral-derivative controller, fly-by-wire systemfunctionality, aircraft brakes, impeller, artificial feel devices, stickshaker, power-by-wire systems, active flow control, thrust vectoring,alerion, landing gear, battery pack, propulsor, management components,control surfaces, sensors/sensor suites, creature comforts, inceptor,throttle, collective, cyclic, yaw pedals, MFDs, PFDs, and the like.Measured state data may exist as analogue and/or digital data,originating from physical CAN bus units such as bits, where a series ofserial binary data are composed and transmitted relaying a measuredstate as indicated from a device located within, on, or communicatingwith the electric aircraft.

Still referring to FIG. 1 , sensor 104 may be configured to detect ameasured force datum. A “measured force datum,” as used in thisdisclosure, is an electrical signal describing information captured bysensor 104 and/or flight component 108. The measured force datum mayinclude temperature, torque, force, pressure, velocity, and the likethereof. Sensor 104 may capture various significant data as describedfor the measured force datum. In a non-limiting embodiment, sensor 104may generate an impact datum 112 as a function of the measured forcedatum. An “impact datum,” as used in this disclosure, is a collection ofinformation describing the performance and qualities of the electricaircraft and its flight components denoting a degree and/or any changeof instantaneous force, torque, kinetic energy, pressure, experienced bythe electric aircraft and its flight components during landing. Impactdatum 112 may include information denoting the change in force and/orenergy experience and/or natural force produced by the landing gear. Forexample and without limitation, impact datum 112 may include anindication of a hard landing. A “hard landing,” as used in thisdisclosure, is a large amount of instantaneous force and/or energyreceived by the landing gear of the electric aircraft. In a non-limitingembodiment, a hard landing may include a determination of the landing ofthe electric aircraft in which the landing gear receives aninstantaneous force reaching and/or exceeding an upper value/limit ofimpact threshold 136. In a non-limiting embodiment, impact datum 112 mayinclude landing data, force magnitude data, altitude data, accelerationdata, and the like thereof. Alternatively or additionally, impact datum112 may include information captured by visual sensors used to detectvisual data. For example and without limitation, impact datum 112 mayinclude 3-D and/or 4-D representation of the potential landing zone ofthe electric aircraft in which sensor 104 may analyze and generateimpact datum 112 aside from external forces. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of thevarious embodiments of generating a collection of data in the context ofmonitoring the landing of the electric aircraft.

With continued reference to FIG. 1 , system 100 may include a computingdevice 116 communicatively connected to sensor 104. Computing device 116may be configured to receive impact datum 112. In a non-limitingembodiment, computing device 116 may receive impact datum 112 via aplurality of physical CAN bus units. In some embodiments, computingdevice 116 may be remotely located from the electric aircraft. This isso, at least in part, to receive data, monitor the electric aircraft,and/or analyze data safely and continuously while the electric aircraftmay undergo various flights or operations. In another embodiment,computing device 116 may be integrated within the electric aircraft. Forexample and without limitation, computing device 116 may include aflight controller. computing device 116 may include any computing deviceas described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. computing device 116 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. computing device 116 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 116 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. computing device 116 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. computing device 116 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. computing device 116 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. computingdevice 116 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

With continued reference to FIG. 1 , computing device 116 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 116 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. computing device 116 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Still referring to FIG. 1 , computing device 116 may be configured tosimulate a landing performance model output 124 as a function of impactdatum 112. A “landing performance model output,” as used in thisdisclosure, is a simulation and/or model of an electric aircraft thatembodies an analytical and/or interactive visualization regarding theimpact experience by the electric aircraft during landing and/ortake-off. Landing performance model 124 may include a plurality of datadescribing the performance of the landing of the electric aircraft andintegrated to landing performance model 124 as a part of impact datum112. In some embodiments, computing device 112 may operate a flightsimulator 120 configured to simulate and/or generate the simulation oflanding performance model output 124. A “flight simulator,” for thepurpose of this disclosure, is a program or set of operations thatsimulate a model of the electric aircraft and its functions. A “virtualrepresentation,” for the purpose of this disclosure, is any model orsimulation which is representative of a physical phenomenon such as theflying of the electric aircraft. For instance and without limitation,flight simulator 120 may be consistent with the flight simulator U.S.patent application Ser. No. 17/348,916 and titled “METHODS AND SYSTEMSFOR SIMULATED OPERATION OF AN ELECTRIC VERTICAL TAKE-OFF AND LANDING(EVTOL) AIRCRAFT” which is incorporated herein in its entirety. In anon-limiting embodiment, landing performance model output 124 mayinclude, at least in part, a virtual representation. As described inthis disclosure, a “virtual representation” includes any model orsimulation accessible by computing device which is representative of aphysical phenomenon, for example without limitation at least a part ofan electric aircraft and its flight component 108 such as its landinggear. For instance and without limitation, the virtual representationmay be consistent with virtual representation in U.S. patent applicationSer. No. 17/348,916. In some cases, the virtual representation may beinteractive with flight simulator 120.

With continued reference to FIG. 1 , computing device 116 may beconfigured to generate a landing performance datum 128. A “landingperformance datum,” as used in this disclosure, is a determination of acollection of data describing the electric aircraft and its flightcomponents such as a landing gear and the performance and quality of thelanding gear. Landing performance datum 128 may include a collection ofinformation describing the external forces the electric aircraft and itsflight component 108 and/or landing gear experiences during landingand/or coming into contact with a surface such as a tarmac. For exampleand without limitation, sensor 104 and/or a strain gauge connected toflight component 108 and/or landing gear may measure the external forcesand/or intensity of the cushioning of a mechanical portion of flightcomponent 108 (e.g. high stress area, knee spring, etc.). Landingperformance datum 128 may include comparisons and determinations relatedto the impact experienced by the electric aircraft and its flightcomponent 108 based on previously measured landing performance datum ofthe electric aircraft, similar electric aircrafts, previous flightoperations, and/or previously generated models. In another non-limitingembodiment, computing device 116 may determine landing performance datum128 as a function of a comparison between landing performance modeloutput 124 of the electric aircraft and a plurality of previouslysimulated landing performance model outputs, wherein the previouslysimulated landing performance model outputs were recorded and/or storedin an impact database 144. An “impact database,” as used in thisdisclosure, is a data storage system used to store a plurality of datumgenerated and/or collected by an electric aircraft. Impact database 144may be implemented, without limitation, as a relational database, akey-value retrieval database such as a NOSQL database, or any otherformat or structure for use as a database that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Impact database 144 may alternatively or additionally beimplemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table or the like. Impact database144 may include a plurality of data entries and/or records as describedabove. Data entries in a database may be flagged with or linked to oneor more additional elements of information, which may be reflected indata entry cells and/or in linked tables such as tables related by oneor more indices in a relational database. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which data entries in a database may store, retrieve, organize,and/or reflect data and/or records as used herein, as well as categoriesand/or populations of data consistently with this disclosure.

Still referring to FIG. 1 , in a non-limiting embodiment, impactdatabase 144 may be local to the electric aircraft and/or onboard theelectric aircraft. In another embodiment, impact database 144 may belocated externally from the electric aircraft such as in the cloud.Impact database 144 may include a plurality of data tables configured tostore every instance a landing performance datum isgenerated/determined. This is so, at least in part to create a robustdata storage system of impact database 144. In another embodiment,determining landing performance datum 128 may include comparing ahistory of landing performance datums of the electric aircraft.Alternatively or additionally, in some embodiments, landing performancedatum 128 may be determined by an impact force magnitude. Computingdevice 116 may be configured to transmit any datum such as landingperformance datum 128 to a remote device. The remote device may include,but not limited to, a server, a cloud database, a cloud service, impactdatabase 144, and/or any device and/or data storage system that isremotely located. In a non-limiting embodiment, the remote device mayinclude user device 148

Still referring to FIG. 1 , landing performance datum 128 may detect ahazardous instance 132. A “hazardous instance,” as used in thisdisclosure, is an event in which one or more straining parameters denotean unsafe health state of flight component 108 such as the landing gearof the electric aircraft. Hazardous instance 132 may indicate repeatedhard landings and/or potential damages of flight component 108. As usedin this disclosure, “straining parameters” are parameters denoting theexternal forces experienced by landing/take-off involving flightcomponents such as the landing gear. In some embodiments, strainingparameters may include temperature, strain, force, and the like thereof.In another embodiment, straining parameters may include overloading,humidity, repeatability, EMI induced errors, hysteresis, linearity, zeroshift with temperature, zero offset, etc. Alternatively or additionally,hazardous instance 132 may include a boolean determination that flightcomponent 108 is degraded and/or damaged to a point where immediateattention to it is required. In a non-limiting embodiment, hazardousinstance 132 may include information denoting too much movement and/ortoo little resistance by shock absorbers and/or retractable gears offlight component 108. In another embodiment, hazardous instance 132 mayinclude a temperature detected by the strain gauge that is too high,indicating severe stress on flight component 108. In another example,hazardous instance 132 may include too much stress or strain detected bythe strain gauge. In another example, landing performance datum 128 mayinclude a poor health state of flight component 108, wherein thehazardous instance is determined as a function of checking the integrityof flight component 108. For example and without limitation, flightcomponent 108 may experience a variety of wear and tear as a result ofrepeated hard landings, wherein an operator may physically check theintegrity of flight component 108 and determine, to the operator's bestjudgement, the health state of flight component and/or whether flightcomponent 108 denotes a hazardous instance. Alternatively oradditionally, hazardous instance 132 may be determined as a function ofthe electric aircraft undergoing a fixed number of landings and/or hardlandings. This is so, at least in part, to standardize a life cycle offlight component 108 such as its landing gear in order to avoidoverusing it. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of the various embodiments denoting apotentially dangerous event related to the landing gears of the electricaircraft for purposes as described herein.

With continued reference to FIG. 1 , hazardous instance 132 may bedetermined as a function of an impact threshold 136. An “impactthreshold,” as used in this disclosure is a level to which a flightcomponent can be exposed to a certain amount of external force withoutadverse effects. In a non-limiting embodiment, impact threshold 132 mayinclude upper and lower values representing a standard deviation inwhich the external forces applied onto flight component 108 mayexperience without causing concern. In another non-limiting embodiment,impact threshold 132 may include an upper value indicating a limit inwhich an external force exceeding that upper value will trigger and/ordetermine hazardous instance 132. In another non-limiting embodiment,impact threshold 132 may include a lower value indicating a limit inwhich an external force exceeding that lower value (from zero) willtrigger and/or determine hazardous instance 132. For example and withoutlimitation, the upper and lower values of impact threshold 136 mayinclude ±3000 microstrain on the landing gear of the electric aircraft.In the event landing performance datum 128 indicates a microstrainexceeding ±3000, hazardous instance 132 may further be determined. Inanother non-limiting example, some impact datum exceeding the uppervalue of impact threshold 136 may indicate a hard landing. Computingdevice 116 may generate an alert datum 140 in the event the upper limitis reached and/or exceeded, indicating a greater probability of imminentand/or incremental damage to the electric aircraft. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof the various embodiments of parameters of a threshold for indicatingsome hazardous instance in the context of impact monitoring.

Still referring to FIG. 1 , impact threshold 136 may be represented as afuzzy set. For example and without limitation, the fuzzy set may includea linguistic variable representing impact severity, where membership ina fuzzy set representing a high or severe impact value may meet thethreshold and/or represent a degree of severity to be used indetermining how serious, hazardous, and/or potentially hazardous theimpact was. An “impact severity,” as used in this disclosure, is ameasure and/or range denoting how severe an impact of the electricaircraft was and/or will be. The impact severity may include the threatlevels. In a non-limiting embodiment, the fuzzy set and/or landingperformance model may be generated as a function of an impact machinelearning model. An “impact machine-learning model,” as used in thisdisclosure, is any machine-learning model used to output the fuzzy set.Alternatively or additionally, the impact machine-learning model mayoutput landing performance model output 124. In a non-limitingembodiment, the impact machine-learning model may be configured to tunethe coefficients of the membership functions of the fuzzy set. Theimpact machine-learning model may receive an input from computing device116 such as a tunable threshold parameter. In a non-limiting embodiment,the tunable threshold parameter may be retrieved from impact database144. A “tunable threshold parameter,” as used in this disclosure, is anyimpact threshold in which the range and/or limits are adjustable and/oradjusted based on flight component 108 and/or the landing gears. Forexample and without limitation, the electric aircraft may be fitted witha new and improved landing gear in which the impact threshold and/orimpact severity is higher. For instance, the new and improved landinggear may be able to withstand greater instantaneous force and/or hardlanding without resulting in serious or hazardous damage to itself ofthe electric aircraft compared to an older model of the landing gear ora landing gear with a long term of service, which may have a range thatis a lower offset of the range of the new and improved landing gear,wherein the older model of the landing gear may be unable to withstandthe instantaneous force and/or hard landing that the new and improvedmodel may withstand. The impact machine-learning model may be trainedusing an impact training set. The impact training set may be receivedfrom impact database 144. An “impact training set,” as used in thisdisclosure is a training set comprising some tunable threshold parametercorrelated to the impact threshold best associated with the electricaircraft and its landing gear. In a non-limiting embodiment, the impacttraining set may include an element of data describing the landing gearcorrelated to an impact threshold for that landing gear. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of the various training sets used to train a machine-learningmodel for outputting the best matched impact threshold.

Still referring to FIG. 1 , hazardous instance 132 may be determined asa function of a hazardous landing machine-learning model. A “hazardouslanding machine-learning model,” as used in this disclosure is anymachine-learning model configured to output some hazardous instance. Ina non-limiting embodiment, computing device 116 may train the hazardouslanding machine-learning model using a hazardous instance training set.A “hazardous instance training set,” as used in this disclosure, is atraining set comprising a previously generated impact datum correlatedto a previously determined hazardous instance. For example and withoutlimitation, the impact training set may include information denotingsome number of repeated hard landings of one or more electric aircraftand/or some value of microstrain measured by the strain gauge exceedinga safe limit correlated to a boolean determination of a hazardousinstance, where such instances may have been input by users and/ormeasured in past flights by one or more aircraft. In a non-limitingembodiment, the hazardous landing machine-learning model may receive theimpact threshold from the impact machine-learning model as an input tooutput the hazardous instance using the hazardous training set. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of the generation of a training set comprising past datacorrelated to a past output for purposes as described herein.

With continued reference to FIG. 1 , computing device 116 may beconfigured to display landing performance datum 128 and/or hazardousinstance on a user device 148. A “user device,” for the purpose of thisdisclosure, is any additional computing device, such as a mobile device,laptop, desktop computer, or the like. In a non-limiting embodiment,user device 148 may be a computer and/or smart phone operated by a userin a remote location. User device 148 may include, without limitation, adisplay in communication with computing device 116; the display mayinclude any display as described in the entirety of this disclosure suchas a light emitting diode (LED) screen, liquid crystal display (LCD),organic LED, cathode ray tube (CRT), touch screen, or any combinationthereof. In a non-limiting embodiment, user device 148 may include agraphical user interface (GUI) configured to display any informationfrom computing device 116 and/or any computing device. In a non-limitingembodiment, user device 148 may be utilized by a user located remotelyfrom the electric aircraft in order to analyze data from the electricaircraft in a remote location. Alternatively or additionally, userdevice 148 may be located inside the electric aircraft for a pilot tointeract with. For example and without limitation, user device 148 mayinclude a pilot device incorporated into the cockpit of the electricaircraft. A “pilot device,” for the purpose of this disclosure, is aninteractive and functional electronic instrument within a physicalcockpit used by a pilot that provides crucial information in flight. Ina non-limiting embodiment, the pilot device may provide information ofthe electric aircraft the pilot is piloting such as, but not limited to,altitude, airspeed, vertical speed, heading and much more other crucialinformation in flight. In a non-limiting embodiment, the pilot devicemay include any computing device consistently with the entirety of thisdisclosure. In another non-limiting embodiment, the pilot device may beconfigured to support avionics and/or simulated avionics to which apersons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of the implementation of avionics for thepurpose of a simulated environment. In a non-limiting embodiment, thepilot device may include a functional primary flight display (PFD), afunctional electronic instrument system (EFIS), a functional electronichorizontal situation indicator (EHSI), and the like thereof. In anon-limiting embodiment, the simulated avionics may include theequipment and electronics to support communication, navigation,multi-system management, and the like thereof.

With continued reference to FIG. 1 , user device 148 may include agraphical user interface (GUI), which may include any displays asdescribed above, including without limitation a concave screen 120. In anon-limiting embodiment, the GUI may be configured to display visualindicators that may be used with electric aircraft, such as but notlimited to, altitude, wind speed, aircraft speed, roll, yaw, pitch,flight component status, torque of a flight component, temperature of abattery, power output of a battery, remaining battery charge, batteryhealth, and/or fuel supply. In another non-limiting embodiment, the GUImay display a flight plan in graphical form. Graphical form may includea two-dimensional plot of two variables that represent data received bythe controller, such as past maneuvers and predicted future maneuvers.In one embodiment, GUI may also display a user's and/or a pilot's inputin real-time. GUI may be configured to show a primary flight display.The primary flight display may include an airspeed indicator, altitudeindicator, slip skid indicator, altimeter, vertical speed indicator(VSI), turn indicator, horizontal situation indicator, and/or a turnrate indicator. In some embodiments, the primary flight display mayinclude a general cruising speed, a ground airspeed, a flap range, abest angle of climb speed, a rotation speed and/or a best rate of climbspeed. The PFD may include a transponder code, air temperature,waypoint, distance to waypoint, time and/or compass. In someembodiments, user device 148 may display a virtual representation of theelectric aircraft and flight component 108, highlighting flightcomponent 108 with bright colors, lines, patterns, and the like thereof.User device 148 may indicate hazardous instance 132 surrounding flightcomponent 108 and display quantitative and/or qualitative informationdescribing it. For example and without limitation, user device 148 maydisplay strain parameters indicating the quality and/or performance offlight component 108 has exceeded impact threshold 136, in which impactthreshold 136 indicates a range of values for which the strainparameters must maintain to be regarded as safe and operable. In someembodiments, user device 148 may display an alert datum 140 indicatingthe potential danger of flight component 108 and its damage ordegradation as a function of hazardous instance 132.

With continued reference to FIG. 1 , computing device 116 may beconfigured to alert a user of hazardous instance 132. In a non-limitingembodiment, computing device 116 may generate an alert datum 140 as afunction of hazardous instance 132 and/or landing performance datum 128which may include an alert. An “alert datum,” as used in thisdisclosure, is a collection of information describing a level ofintensity and/or priority of an instance of a potentially dangerousevent. Alert datum 140 may include a quick notification of a potentiallydangerous event. In a non-limiting embodiment, alert datum 140 mayinclude a range of alerts comprising of a combination of sounds and/orimages indicating hazardous instance 132 at various threat levels. A“threat level,” as used in this disclosure is an instance denoting achance of a hazardous event. The various threat levels may indicate arange of a low chance of a hazardous event to a high chance of ahazardous event. In a non-limiting embodiment, alert datum 140 mayinclude a warning symbol to a user and/or pilot, notifying them thatflight component 108 is potentially dangerous as a function of repeatedhard landings, degraded materials resulting from long term use, and thelike thereof. A “warning symbol,” for the purpose of this disclosure, isan indicative sign of a potentially dangerous event associated withflight component 108 of the electric aircraft. The warning symbol mayinclude an abbreviation, a sign, or combination thereof. The warningsymbol may highlight itself in blinking form, distinct colors, orcombination thereof. Examples of warning symbols may indicate, but notlimited to, a malfunction or failure of at least flight component 108,landing gears, components of the landing gears, and the like thereof.The warning symbol or plurality of warning symbols may dissuade thepilot from undertaking a disadvantageous action. Examples ofdisadvantageous actions include, but not limited to, at least actionsthat may harm the electric aircraft, the flight components, actions thatmay produce collateral damage, and the like. In a non-limitingembodiment, user device 148 may include a locator component. A “locatorcomponent,” as used in this disclosure, is a device and/or componentthat a pilot can use to point a cursor at a point on the GUI of userdevice 148. The locator component may include without limitation a wiredor wireless mouse, a touchpad, a touchscreen, a game controller, or thelike. The locator component may include a motion-capture device, such aswithout limitation a device that tracks motion of offsite surgeon'shands optically and/or using a sensor of motion, which may beimplemented in any way suitable for implementation of sensor 104 asdescribed above.

Still referring to FIG. 1 , alert datum 140 may include one or moreclassifications of severity of hazardous instance 132. For example andwithout limitation, alert datum 140 may indicate through user device 148that flight component 108 may pose some threat to the electric aircraftas a function of predictive models produced in part by computing device116. Computing device 116 may generate alert datum 140 which may includea mild level of priority. In another non-limiting example, flightcomponent 108 may experience a hard landing causing severe damage and/ordegradation to it. The damage and/or degradation may have exceeded anypredicted damage and/or degradation computing device 116 may havegenerated. Computing device 116 may be configured to generate alertdatum 140 comprising a high level of priority for immediate action to betaken on flight component 108. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of the variousclassifications and priorities for potential damage in the context ofmonitoring impact for an electric aircraft.

With continued reference to FIG. 1 , computing device 116 may beconfigured to initiate an emergency landing as a function of alert datum140. Alert datum 140 may include a serious hazardous instance 132 toflight component 108. In a non-limiting embodiment, the electricaircraft may be cruising in the air. The electric aircraft mayexperience an unexpected event in which a component of flight component108 such as a landing gear detaches from the electric aircraftmid-flight. Sensor 104 may detect this incident in which alert datum 140may be generated and alert the pilot and/or remotely located user of ahigh priority event. For instance and without limitation, computingdevice 116 may generate an emergency landing to be taken place in orderto respond to this hazardous instance 132. In a non-limiting embodiment,computing device 116 may generate a program and/or button for which apilot and/or user to interact with via the GUI of user device 148 tobegin the emergency landing. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of the variousembodiments of emergency protocols and the causes of them in the contextof landing gear malfunctions.

Now referring to FIG. 2 , an exemplary embodiment of fuzzy setcomparison 200 for an impact threshold is illustrated. The impactthreshold may be consistent with impact threshold 136 in FIG. 1 . Afirst fuzzy set 204 may be represented, without limitation, according toa first membership function 208 representing a probability that an inputfalling on a first range of values 212 is a member of the first fuzzyset 204, where the first membership function 208 has values on a rangeof probabilities such as without limitation the interval [0,1], and anarea beneath the first membership function 208 may represent a set ofvalues within first fuzzy set 204. Although first range of values 212 isillustrated for clarity in this exemplary depiction as a range on asingle number line or axis, first range of values 212 may be defined ontwo or more dimensions, representing, for instance, a Cartesian productbetween a plurality of ranges, curves, axes, spaces, dimensions, or thelike. First membership function 208 may include any suitable functionmapping first range 212 to a probability interval, including withoutlimitation a triangular function defined by two linear elements such asline segments or planes that intersect at or below the top of theprobability interval. As a non-limiting example, triangular membershipfunction may be defined as:

${y( {x,a,b,c} )} = \{ \begin{matrix}{0,{{{for}x} > {c{and}x} < a}} \\{\frac{x - a}{b - a},{{{for}a} \leq x < b}} \\{\frac{c - x}{c - b},{{{if}b} < x \leq c}}\end{matrix} $

a trapezoidal membership function may be defined as:

${y( {x,a,b,c,d} )} = {\max( {{\min( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} )},0} )}$

a sigmoidal function may be defined as:

${y( {x,a,c} )} = \frac{1}{1 - e^{- {a({x - c})}}}$

a Gaussian membership function may be defined as:

${y( {x,c,\sigma} )} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$

and a bell membership function may be defined as:

${y( {x,a,b,c} )} = \lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \rbrack^{- 1}$

Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

First fuzzy set 204 may represent any value or combination of values asdescribed above, including predictive prevalence value, probabilisticoutcome, any resource datum, any niche datum, and/or any combination ofthe above. A second fuzzy set 216, which may represent any value whichmay be represented by first fuzzy set 204, may be defined by a secondmembership function 220 on a second range 224; second range 224 may beidentical and/or overlap with first range 212 and/or may be combinedwith first range via Cartesian product or the like to generate a mappingpermitting evaluation overlap of first fuzzy set 204 and second fuzzyset 216. Where first fuzzy set 204 and second fuzzy set 216 have aregion 228 that overlaps, first membership function 208 and secondmembership function 220 may intersect at a point 232 representing aprobability, as defined on probability interval, of a match betweenfirst fuzzy set 204 and second fuzzy set 216. Alternatively oradditionally, a single value of first and/or second fuzzy set may belocated at a locus 236 on first range 212 and/or second range 224, wherea probability of membership may be taken by evaluation of firstmembership function 208 and/or second membership function 220 at thatrange point. A probability at 228 and/or 232 may be compared to athreshold 240 to determine whether a positive match is indicated.Threshold 240 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 204 and second fuzzy set 216, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between probabilisticoutcomes and/or predictive prevalence values 112 for combination tooccur as described above. There may be multiple thresholds; forinstance, a second threshold may indicate a sufficient match forpurposes impact threshold 136 as described in this disclosure. Eachthreshold may be established by one or more user inputs. Alternativelyor additionally, each threshold may be tuned by a machine-learningand/or statistical process, for instance and without limitation asdescribed in further detail below.

In an embodiment, a degree of match between fuzzy sets may be used torank one resource against another. For instance, if landing performancedatum 128 and a previous landing performance datum from history oflanding performance datums have fuzzy sets matching a probabilisticoutcome fuzzy set by having a degree of overlap exceeding threshold 136and/or upper/lower values, computing device 116 may further rank the tworesources by ranking a resource having a higher degree of match morehighly than a resource having a lower degree of match. Where multiplefuzzy matches are performed, degrees of match for each respective fuzzyset may be computed and aggregated through, for instance, addition,averaging, or the like, to determine an overall degree of match, whichmay be used to rank resources; selection between two or more matchingresources may be performed by selection of a highest-ranking resource,and/or multiple landing performance datums 128 may be presented to auser in order of ranking.

Referring now to FIG. 3 , an exemplary embodiment of comparison ofbivalent sets on ranges for an impact threshold is illustrated. A firstbivalent set 304 may be defined on a first range 308, which may have anyform suitable for use as a first range 512 for a fuzzy set as describedabove. In an embodiment, first bivalent set 304 may be defined accordingto a first characteristic function 312, which may include, withoutlimitation, a step function having output values on a probabilityinterval such as [0,1] or the like; step function may have an outputrepresenting 100% or probability of 1 for values falling on first range308 and zero or a representation of zero probability for values not onfirst range 308. A second bivalent set 316 may be defined on a secondrange 320, which may include any range suitable for use as first range308. Second bivalent set may be defined by a second characteristicfunction 324, which may include any function suitable for use as firstcharacteristic function 312. In an embodiment a match between firstbivalent set 308 and second bivalent set 320 may be established wherefirst range 308 intersects second range 320, and/or where firstcharacteristic function 312 and second characteristic function 324 shareat least one point in first range 308 and second range 316 at which bothfirst characteristic function 312 and second characteristic function 324are non-zero.

Referring now to FIG. 4 , an exemplary embodiment of an aircraft 400 isillustrated. In an embodiment, aircraft 400 is an electric aircraft. Asused in this disclosure an “aircraft” is any vehicle that may fly bygaining support from the air. As a non-limiting example, aircraft mayinclude airplanes, helicopters, commercial and/or recreationalaircrafts, instrument flight aircrafts, drones, electric aircrafts,airliners, rotorcrafts, vertical takeoff and landing aircrafts, jets,airships, blimps, gliders, paramotors, and the like. Aircraft 400 mayinclude an electrically powered aircraft. In embodiments, electricallypowered aircraft may be an electric vertical takeoff and landing (eVTOL)aircraft. Electric aircraft may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. Electric aircraft may include one or more mannedand/or unmanned aircrafts. Electric aircraft may include one or moreall-electric short takeoff and landing (eSTOL) aircrafts. For example,and without limitation, eSTOL aircrafts may accelerate plane to a flightspeed on takeoff and decelerate plane after landing. In an embodiment,and without limitation, electric aircraft may be configured with anelectric propulsion assembly. Electric propulsion assembly may includeany electric propulsion assembly as described in U.S. Nonprovisionalapplication Ser. No. 16/603,225, and titled “AN INTEGRATED ELECTRICPROPULSION ASSEMBLY,” the entirety of which is incorporated herein byreference.

With continued reference to FIG. 4 , as used in this disclosure, avertical take-off and landing (VTOL) aircraft is an aircraft that canhover, take off, and land vertically. An eVTOL, as used in thisdisclosure, is an electrically powered aircraft typically using anenergy source, of a plurality of energy sources to power aircraft. Tooptimize the power and energy necessary to propel aircraft 400, eVTOLmay be capable of rotor-based cruising flight, rotor-based takeoff,rotor-based landing, fixed-wing cruising flight, airplane-style takeoff,airplane style landing, and/or any combination thereof. Rotor-basedflight, as described herein, is where the aircraft generates lift andpropulsion by way of one or more powered rotors or blades coupled withan engine, such as a “quad-copter,” multi-rotor helicopter, or othervehicle that maintains its lift primarily using downward thrustingpropulsors. “Fixed-wing flight,” as described herein, is where theaircraft is capable of flight using wings and/or foils that generatelift caused by the aircraft's forward airspeed and the shape of thewings and/or foils, such as airplane-style flight.

Still referring to FIG. 4 , as used in this disclosure a “fuselage” is amain body of an aircraft, or in other words, the entirety of theaircraft except for a cockpit, nose, wings, empennage, nacelles, any andall control surfaces, and generally contains an aircraft's payload.Fuselage 404 may include structural elements that physically support ashape and structure of an aircraft. Structural elements may take aplurality of forms, alone or in combination with other types. Structuralelements may vary depending on a construction type of aircraft such aswithout limitation a fuselage 404. Fuselage 404 may include a trussstructure. A truss structure may be used with a lightweight aircraft andincludes welded steel tube trusses. A “truss,” as used in thisdisclosure, is an assembly of beams that create a rigid structure, oftenin combinations of triangles to create three-dimensional shapes. A trussstructure may alternatively include wood construction in place of steeltubes, or a combination thereof. In embodiments, structural elements mayinclude steel tubes and/or wood beams. In an embodiment, and withoutlimitation, structural elements may include an aircraft skin. Aircraftskin may be layered over the body shape constructed by trusses. Aircraftskin may include a plurality of materials such as plywood sheets,aluminum, fiberglass, and/or carbon fiber, the latter of which will beaddressed in greater detail later herein.

In embodiments, and with continued reference to FIG. 4 , aircraftfuselage 404 may include and/or be constructed using geodesicconstruction. Geodesic structural elements may include stringers woundabout formers (which may be alternatively called station frames) inopposing spiral directions. A “stringer,” as used in this disclosure, isa general structural element that may include a long, thin, and rigidstrip of metal or wood that is mechanically coupled to and spans adistance from, station frame to station frame to create an internalskeleton on which to mechanically couple aircraft skin. A former (orstation frame) may include a rigid structural element that is disposedalong a length of an interior of aircraft fuselage 404 orthogonal to alongitudinal (nose to tail) axis of the aircraft and may form a generalshape of fuselage 404. A former may include differing cross-sectionalshapes at differing locations along fuselage 404, as the former is thestructural element that informs the overall shape of a fuselage 404curvature. In embodiments, aircraft skin may be anchored to formers andstrings such that the outer mold line of a volume encapsulated byformers and stringers includes the same shape as aircraft 400 wheninstalled. In other words, former(s) may form a fuselage's ribs, and thestringers may form the interstitials between such ribs. The spiralorientation of stringers about formers may provide uniform robustness atany point on an aircraft fuselage such that if a portion sustainsdamage, another portion may remain largely unaffected. Aircraft skin maybe attached to underlying stringers and formers and may interact with afluid, such as air, to generate lift and perform maneuvers.

Still referring to FIG. 4 , aircraft 400 may include a plurality offlight components 408. As used in this disclosure a “flight component”is a component that promotes flight and guidance of an aircraft. In anembodiment, flight component 408 may be mechanically coupled to anaircraft. Plurality of flight components 408 may include flightcomponent 108. As used herein, a person of ordinary skill in the artwould understand “mechanically coupled” to mean that at least a portionof a device, component, or circuit is connected to at least a portion ofthe aircraft via a mechanical coupling. Said mechanical coupling mayinclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling may be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling may be used to join two pieces ofrotating electric aircraft components.

Still referring to FIG. 4 , plurality of flight components 408 mayinclude at least a landing gear. The landing gear may be consistent withany landing gear as described in the entirety of this disclosure. Inanother embodiment, plurality of flight components 408 may include atleast a propulsor. As used in this disclosure a “propulsor” is acomponent and/or device used to propel a craft upward by exerting forceon a fluid medium, which may include a gaseous medium such as air or aliquid medium such as water. Propulsor may include any device orcomponent that consumes electrical power on demand to propel an electricaircraft in a direction or other vehicle while on ground or in-flight.For example, and without limitation, propulsor may include a rotor,propeller, paddle wheel and the like thereof. In an embodiment,propulsor may include a plurality of blades. As used in this disclosurea “blade” is a propeller that converts rotary motion from an engine orother power source into a swirling slipstream. In an embodiment, blademay convert rotary motion to push the propeller forwards or backwards.In an embodiment propulsor may include a rotating power-driven hub, towhich are attached several radial airfoil-section blades such that thewhole assembly rotates about a longitudinal axis.

In an embodiment, and still referring to FIG. 4 , plurality of flightcomponents 408 may include one or more power sources. As used in thisdisclosure a “power source” is a source that that drives and/or controlsany other flight component. For example, and without limitation powersource may include a motor that operates to move one or more liftpropulsor components, to drive one or more blades, or the like thereof.A motor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof. A motormay also include electronic speed controllers or other components forregulating motor speed, rotation direction, and/or dynamic braking. Inan embodiment, power source may include an inverter. As used in thisdisclosure an “inverter” is a device that changes one or more currentsof a system. For example, and without limitation, inverter may includeone or more electronic devices that change direct current to alternatingcurrent. As a further non-limiting example, inverter may includereceiving a first input voltage and outputting a second voltage, whereinthe second voltage is different from the first voltage. In anembodiment, and without limitation, inverter may output a waveform,wherein a waveform may include a square wave, sine wave, modified sinewave, near sine wave, and the like thereof.

Still referring to FIG. 4 , plurality of flight components 408 mayinclude a pusher component. As used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like. Additionally, or alternatively, pusher flight componentmay include a plurality of pusher flight components. Pusher componentmay be configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. For example, forward thrustmay include a force of 1045 N to force aircraft to in a horizontaldirection along the longitudinal axis. As a further non-limitingexample, pusher component may twist and/or rotate to pull air behind itand, at the same time, push aircraft 400 forward with an equal amount offorce. In an embodiment, and without limitation, the more air forcedbehind aircraft, the greater the thrust force with which aircraft 400 ispushed horizontally will be. In another embodiment, and withoutlimitation, forward thrust may force aircraft 400 through the medium ofrelative air. Additionally or alternatively, plurality of flightcomponents 408 may include one or more puller components. As used inthis disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

Referring now to FIG. 5 , a flow diagram of an exemplary embodiment of amethod 500 for monitoring impact on an electric aircraft is provided.Method 500, at step 505, may include detecting, by a sensorcommunicatively connected to a flight component, a measured force datum.The sensor may include any sensor as described herein. The measuredforce datum may include any measured force datum as described herein.The flight component may be consistent with any flight component asdescribed in the entirety of this disclosure. In a non-limitingembodiment, method 505 may include detecting, by a strain gaugeconnected to a landing gear of an electric aircraft, the measured forcedatum. In a non-limiting embodiment, the electric aircraft may includean electric vertical take-off and landing (eVTOL) aircraft. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of the various methods for measuring data from sensing devicesin the context of an electric aircraft.

Still referring to FIG. 5 , method 500, at step 510, may includegenerating an impact datum as a function of the measured force datum.The impact datum may be consistent with any impact datum as described inthe entirety of this disclosure. In a non-limiting embodiment, thesensor may include any computing device configured to convert electricalsignals into readable data. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of the various methods ofconverting signals representing data into a collection of informationfor analysis purposes in the context of monitoring impact of an electricaircraft.

With continued reference to FIG. 5 , method 500, at step 515, mayinclude simulating, by a computing device, a landing performance modeloutput as a function of the impact datum. The computing device may beconsistent with any computing device as described in the entirety ofthis disclosure. The landing performance model output may be consistentwith any landing performance output as described herein. In anon-limiting embodiment, method 500 may include receiving the impactdatum as a function of a plurality of physical CAN bus units. In anotherembodiment, method 500 may include simulating the landing performancemodel output as a function of a flight simulator. The flight simulatormay include any flight simulator as described herein. In a non-limitingembodiment, the method may include generating a virtual representationof the electric aircraft and/or the flight component such as the landinggear as a function of the flight simulator. The virtual representationmay include any virtual representation as described herein. Thecomputing device may receive parameters from the impact datum andgenerate a model and/or virtual representation of the impact datum. In anon-limiting embodiment, the flight simulator may simulate a model thatvisualizes potential landings of the electric aircraft and the effectsof the landing gears, indicating a change in performance.

Still referring to FIG. 5 , method 500, at step 520, may includegenerating a landing performance datum as a function of a comparisonbetween the landing performance model output of the electric aircraftand a plurality of landing performance model outputs from an impactdatabase. The landing performance datum may be consistent with anylanding performance datum as described in the entirety of thisdisclosure. In a non-limiting embodiment, determining the landingperformance datum may include identifying and/or determining a hazardousinstance. The hazardous instance may include any hazardous instance asdescribed herein. In a non-limiting embodiment, the comparison mayinclude comparing a history of previously generated landing performancedatums wherein the history of landing performance datums are recordedand/or stored in the impact database. The impact database may includeany impact database as described herein. Method 500 may includetransmitting any datum such as the landing performance datum to remotedevice, wherein transmitting further includes storing any datum in aremotely located server, a cloud database, impact database, and the likethereof, in a non-limiting embodiment, the hazardous instance may bedetermined as a function of an impact threshold. The impact thresholdmay be consistent with any impact threshold as described in the entiretyof this disclosure.

With continued reference to FIG. 5 , method 500, at step 525, mayinclude determining an alert datum as a function of the landingperformance datum. An alert datum may be consistent with any alert datumas described herein. The alert datum may include an alert that indicatesa level of severity of the impact of landing, such as a hard landing.

With continued reference to FIG. 5 , method 500, at step 530, mayinclude displaying the landing performance datum on a user device. Theuser device may include any user device as described herein. In anon-limiting embodiment, method 500 may include displaying the landingperformance datum and/or the hazardous instance. The displaying me bedone on a pilot device located inside the cockpit of the electricaircraft. The pilot device may include any pilot device as describedherein. In a non-limiting embodiment, method 500 may include initiatingan emergency landing as a function of the alert in the even the electricaircraft is in the air. The computing device may have somepre-programmed command to support any emergency landing in which a userand/or pilot may interact with a GUI on the user device to perform thelanding. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of the various methods for displayinginformation and performing emergency landings in the context ofmonitoring impact of the landing gears of an electric aircraft.

Now referring to FIG. 6 , an exemplary embodiment 600 of a flightcontroller 604 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 604 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 604may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 604 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 6 , flight controller 604may include a signal transformation component 608. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 608 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component608 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 6-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 608 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 608 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 608 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 6 , signal transformation component 608 may beconfigured to optimize an intermediate representation 612. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 608 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 608 may optimizeintermediate representation 612 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 608 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 608 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 604. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 608 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 6 , flight controller 604may include a reconfigurable hardware platform 616. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 616 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 6 , reconfigurable hardware platform 616 mayinclude a logic component 620. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 620 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 620 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 620 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 620 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 620 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 612. Logiccomponent 620 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 604. Logiccomponent 620 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 620 may beconfigured to execute the instruction on intermediate representation 612and/or output language. For example, and without limitation, logiccomponent 620 may be configured to execute an addition operation onintermediate representation 612 and/or output language.

In an embodiment, and without limitation, logic component 620 may beconfigured to calculate a flight element 624. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 624 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 624 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 624 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 6 , flight controller 604 may include a chipsetcomponent 628. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 628 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 620 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 628 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 620 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 628 maymanage data flow between logic component 620, memory cache, and a flightcomponent 632. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 632 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component632 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 628 may be configured to communicate witha plurality of flight components as a function of flight element 624.For example, and without limitation, chipset component 628 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 6 , flight controller 604may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 604 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 624. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 604 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 604 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 6 , flight controller 604may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 624 and a pilot signal636 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 636may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 636 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 636may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 636 may include an explicitsignal directing flight controller 604 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 636 may include an implicit signal, wherein flight controller 604detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 636 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 636 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 636 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 636 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal636 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 6 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 604 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 604.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 6 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 604 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 6 , flight controller 604 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 604. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 604 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 604 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 6 , flight controller 604 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 6 , flight controller 604may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller604 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 604 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 604 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 6 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 632. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 6 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 604. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 612 and/or output language from logiccomponent 620, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 6 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 6 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 6 , flight controller 604 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 604 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 6 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 6 , flight controller may include asub-controller 640. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 604 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 640may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 640 may include any component of any flightcontroller as described above. Sub-controller 640 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 640may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 640 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 6 , flight controller may include aco-controller 644. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 604 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 644 mayinclude one or more controllers and/or components that are similar toflight controller 604. As a further non-limiting example, co-controller644 may include any controller and/or component that joins flightcontroller 604 to distributer flight controller. As a furthernon-limiting example, co-controller 644 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 604 to distributed flight control system. Co-controller 644may include any component of any flight controller as described above.Co-controller 644 may be implemented in any manner suitable forimplementation of a flight controller as described above. In anembodiment, and with continued reference to FIG. 6 , flight controller604 may be designed and/or configured to perform any method, methodstep, or sequence of method steps in any embodiment described in thisdisclosure, in any order and with any degree of repetition. Forinstance, flight controller 604 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 7 , an exemplary embodiment of a machine-learningmodule 700 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 704 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 708 given data provided as inputs 712;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 7 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 704 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 704 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 704 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 704 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 704 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 704 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data704 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 7 ,training data 704 may include one or more elements that are notcategorized; that is, training data 704 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 704 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 704 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 704 used by machine-learning module 700 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, an impact datum may be an input used to output a landingperformance datum and/or a hazardous instance.

Further referring to FIG. 7 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 716. Training data classifier 716 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 700 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 704. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 716 may classify elements of training data to thevarious levels of severity and/or priority based on the hazardousinstance for which a subset of training data may be selected for thegenerating of unique alerts.

Still referring to FIG. 7 , machine-learning module 700 may beconfigured to perform a lazy-learning process 720 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 704. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 704 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 7 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 724. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 724 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 724 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 704set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 7 , machine-learning algorithms may include atleast a supervised machine-learning process 728. At least a supervisedmachine-learning process 728, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs as described in this disclosure as inputs, outputs asdescribed in this disclosure as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 704. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process728 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 7 , machine learning processes may include atleast an unsupervised machine-learning processes 732. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 7 , machine-learning module 700 may be designedand configured to create a machine-learning model 724 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 7 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A system for monitoring impact on an electric aircraft, the systemcomprising: at least a sensor communicatively connected to a flightcomponent, the at least a sensor comprising at least a strain gauge,wherein the at least a sensor is configured to: detect a strainparameter of the flight component; detect a measured force datum; andgenerate an impact datum as a function of the measured force datum; anda computing device, the computing device configured to: determine animpact threshold for the flight, wherein the impact threshold includes atunable threshold parameter; simulate a landing performance model outputas a function of the impact datum and the impact threshold, wherein thelanding performance model is generated as a function of an impactmachine learning model, and wherein the impact machine learning model istrained by utilizing impact training set; generate a landing performancedatum as a function of the landing performance model output; determinean alert datum as a function of the landing performance datum, the alertdatum describing a magnitude of an instance of a potentially dangerousevent; and display the landing performance datum and a performance ofthe flight component as a function of the strain parameter on a userdevice.
 2. The system of claim 1, wherein the electric aircraftcomprises an electric vertical take-off and landing aircraft.
 3. Thesystem of claim 1, wherein the flight component comprises a landinggear.
 4. (canceled)
 5. The system of claim 1, wherein the computingdevice is further configured to: detect a hazardous instance as afunction of the landing performance datum; and alert a user, by the userdevice, of the hazardous instance.
 6. The system of claim 5, wherein thehazardous instance is determined as a function of the impact threshold.7. The system of claim 5, wherein the computing device is configured toinitiate an emergency landing as a function of the alert.
 8. The systemof claim 1, wherein the computing device is further configured to recordthe landing performance datum into the impact database.
 9. The system ofclaim 1, wherein the landing performance model output is simulated as afunction of a flight simulator.
 10. The system of claim 1, wherein thecomputing device is further configured to transmit the landingperformance datum to a remote device.
 11. A method for monitoring impacton an electric aircraft, the method comprising: detecting, by a sensorcommunicatively connected to a flight component, a measured force datumand a strain parameter of the flight component; generating an impactdatum as a function of the measured force datum; determining, by thecomputing device, an impact threshold for the flight component, whereinthe impact threshold includes a tunable threshold parameter; simulating,by a computing device, a landing performance model output as a functionof the impact datum and the impact threshold, wherein the landingperformance model is generated as a function of an impact machinelearning model, and wherein the impact machine learning model is trainedby utilizing impact training set; generating a landing performance datumas a function of a comparison between the landing performance modeloutput of the electric aircraft and a plurality of landing performancemodel outputs from an impact database; determining an alert datum as afunction of the landing performance datum, the alert datum describing amagnitude of an instance of a potentially dangerous event; anddisplaying the landing performance datum and a performance of the flightcomponent as a function of the strain parameter on a user device. 12.The method of claim 11, wherein the electric aircraft comprises anelectric vertical take-off and landing aircraft.
 13. The method of claim11, wherein the flight component comprises a landing gear. 14.(canceled)
 15. The method of claim 11, wherein the method furthercomprises: detecting a hazardous instance as a function of the landingperformance datum; and alerting, by the user device, a user as afunction of the hazardous instance.
 16. The method of claim 15, whereinmethod further comprises determining the hazardous instance as afunction of the impact threshold.
 17. The method of claim 15, whereinthe method further comprises initiating an emergency landing as afunction of the alert in the even the electric aircraft is in the air.18. The method of claim 11, wherein the method further comprisesrecording the landing performance datum into the impact database. 19.The method of claim 11, wherein the method further comprises simulatingthe landing performance model output as a function of a flightsimulator.
 20. The method of claim 11, the method further comprisestransmitting the landing performance datum to a remote device.
 21. Thesystem of claim 5, wherein computing device is further configured todetect a hazardous instance as a function of the straining parameter.22. The method of claim 15, wherein method further comprises determiningthe hazardous instance as a function of the straining parameter.